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* support entropy * fix bug --------- Co-authored-by: YuBaoku <49938469+EmmonsCurse@users.noreply.github.com>
213 lines
8.5 KiB
Python
213 lines
8.5 KiB
Python
# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import paddle
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from fastdeploy.model_executor.entropy_utils import (
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calculate_logits_entropy,
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speculate_calculate_logits_entropy,
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)
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class TestCalculateLogitsEntropy(unittest.TestCase):
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def test_basic_functionality(self):
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share_inputs = {
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"seq_lens_this_time": paddle.to_tensor([[1], [0], [15]], dtype="int32"),
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"seq_lens_encoder": paddle.to_tensor([[0], [0], [15]], dtype="int32"),
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"entropy_list": [[], [], []],
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"stop_flags": paddle.to_tensor([[False], [True], [False]], dtype="bool"),
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"req_ids": ["req_1", "req_2", "req_3"],
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}
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logits = paddle.to_tensor(
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[
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[10.0, 1.0, 1.0],
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[1.0, 1.0, 10.0],
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],
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dtype="float32",
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)
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temperature = paddle.ones([3], dtype="float32")
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calculate_logits_entropy(logits, share_inputs, temperature)
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self.assertEqual(len(share_inputs["entropy_list"][0]), 1)
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self.assertEqual(len(share_inputs["entropy_list"][1]), 0)
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self.assertEqual(len(share_inputs["entropy_list"][2]), 1)
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self.assertAlmostEqual(share_inputs["entropy_list"][0][0], 0.0024676250759512186, places=6)
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self.assertAlmostEqual(share_inputs["entropy_list"][2][0], 0.0024676250759512186, places=6)
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def test_temperature_effect(self):
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share_inputs = {
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"seq_lens_this_time": paddle.to_tensor([[1], [0], [15]], dtype="int32"),
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"seq_lens_encoder": paddle.to_tensor([[0], [0], [15]], dtype="int32"),
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"entropy_list": [[], [], []],
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"stop_flags": paddle.to_tensor([[False], [True], [False]], dtype="bool"),
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"req_ids": ["req_1", "req_2", "req_3"],
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}
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logits = paddle.to_tensor(
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[
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[10.0, 1.0, 1.0],
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[1.0, 1.0, 10.0],
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],
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dtype="float32",
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)
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temperature = paddle.to_tensor([[0.8], [1.0], [0.8]], dtype="float32")
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calculate_logits_entropy(logits, share_inputs, temperature)
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self.assertEqual(len(share_inputs["entropy_list"][0]), 1)
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self.assertEqual(len(share_inputs["entropy_list"][1]), 0)
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self.assertEqual(len(share_inputs["entropy_list"][2]), 1)
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self.assertAlmostEqual(share_inputs["entropy_list"][0][0], 0.0003187173861078918, places=6)
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self.assertAlmostEqual(share_inputs["entropy_list"][2][0], 0.0003187173861078918, places=6)
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def test_entropy_list_clear(self):
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share_inputs = {
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"seq_lens_this_time": paddle.to_tensor([[1], [0], [15]], dtype="int32"),
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"seq_lens_encoder": paddle.to_tensor([[0], [0], [15]], dtype="int32"),
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"entropy_list": [[], [], []],
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"stop_flags": paddle.to_tensor([[True], [True], [False]], dtype="bool"),
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"req_ids": ["req_1", "req_2", "req_3"],
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}
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logits = paddle.to_tensor(
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[
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[10.0, 1.0, 1.0],
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[1.0, 1.0, 10.0],
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],
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dtype="float32",
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)
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temperature = paddle.to_tensor([[0.8], [1.0], [0.8]], dtype="float32")
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calculate_logits_entropy(logits, share_inputs, temperature)
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self.assertEqual(len(share_inputs["entropy_list"][0]), 0)
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self.assertEqual(len(share_inputs["entropy_list"][1]), 0)
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self.assertEqual(len(share_inputs["entropy_list"][2]), 1)
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self.assertAlmostEqual(share_inputs["entropy_list"][2][0], 0.0003187173861078918, places=6)
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class TestSpeculateCalculateLogitsEntropy(unittest.TestCase):
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def test_basic_functionality(self):
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share_inputs = {
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"seq_lens_this_time": paddle.to_tensor([[2], [2], [0], [15]], dtype="int32"),
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"seq_lens_encoder": paddle.to_tensor([[0], [0], [0], [15]], dtype="int32"),
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"entropy_list": [[], [], [], []],
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"stop_flags": paddle.to_tensor([[False], [False], [True], [False]], dtype="bool"),
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"req_ids": ["req_1", "req_2", "req_3", "req_4"],
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"accept_num": paddle.to_tensor([2, 1, 0, 0], dtype="int32"), # 推理接受数量
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}
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logits = paddle.to_tensor(
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[
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[10.0, 1.0, 1.0],
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[1.0, 10.0, 1.0],
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[1.0, 1.0, 10.0],
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[1.0, 1.0, 10.0],
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],
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dtype="float32",
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)
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temperature = paddle.ones([3], dtype="float32")
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speculate_calculate_logits_entropy(logits, share_inputs, temperature)
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print(share_inputs["entropy_list"])
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self.assertEqual(len(share_inputs["entropy_list"][0]), 2)
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self.assertEqual(len(share_inputs["entropy_list"][1]), 1)
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self.assertEqual(len(share_inputs["entropy_list"][2]), 0)
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self.assertEqual(len(share_inputs["entropy_list"][3]), 0)
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self.assertAlmostEqual(share_inputs["entropy_list"][0][0], 0.0024676250759512186, places=6)
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self.assertAlmostEqual(share_inputs["entropy_list"][0][1], 0.0024676250759512186, places=6)
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self.assertAlmostEqual(share_inputs["entropy_list"][1][0], 0.0024676250759512186, places=6)
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def test_temperature_effect(self):
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share_inputs = {
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"seq_lens_this_time": paddle.to_tensor([[2], [2], [0], [15]], dtype="int32"),
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"seq_lens_encoder": paddle.to_tensor([[0], [0], [0], [15]], dtype="int32"),
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"entropy_list": [[], [], [], []],
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"stop_flags": paddle.to_tensor([[False], [False], [True], [False]], dtype="bool"),
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"req_ids": ["req_1", "req_2", "req_3", "req_4"],
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"accept_num": paddle.to_tensor([2, 1, 0, 0], dtype="int32"), # 推理接受数量
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}
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logits = paddle.to_tensor(
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[
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[10.0, 1.0, 1.0],
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[1.0, 10.0, 1.0],
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[1.0, 1.0, 10.0],
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[1.0, 1.0, 10.0],
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],
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dtype="float32",
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)
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temperature = paddle.to_tensor([[0.8], [0.8], [0.8], [0.8]], dtype="float32")
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speculate_calculate_logits_entropy(logits, share_inputs, temperature)
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print(share_inputs["entropy_list"])
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self.assertEqual(len(share_inputs["entropy_list"][0]), 2)
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self.assertEqual(len(share_inputs["entropy_list"][1]), 1)
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self.assertEqual(len(share_inputs["entropy_list"][2]), 0)
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self.assertEqual(len(share_inputs["entropy_list"][3]), 0)
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self.assertAlmostEqual(share_inputs["entropy_list"][0][0], 0.0003187173861078918, places=6)
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self.assertAlmostEqual(share_inputs["entropy_list"][0][1], 0.0003187173861078918, places=6)
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self.assertAlmostEqual(share_inputs["entropy_list"][1][0], 0.0003187173861078918, places=6)
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def test_entropy_list_clear(self):
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share_inputs = {
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"seq_lens_this_time": paddle.to_tensor([[2], [2], [0], [15]], dtype="int32"),
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"seq_lens_encoder": paddle.to_tensor([[0], [0], [0], [15]], dtype="int32"),
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"entropy_list": [[], [], [], []],
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"stop_flags": paddle.to_tensor([[True], [False], [True], [False]], dtype="bool"),
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"req_ids": ["req_1", "req_2", "req_3", "req_4"],
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"accept_num": paddle.to_tensor([2, 1, 0, 0], dtype="int32"), # 推理接受数量
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}
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logits = paddle.to_tensor(
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[
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[10.0, 1.0, 1.0],
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[1.0, 10.0, 1.0],
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[1.0, 1.0, 10.0],
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[1.0, 1.0, 10.0],
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],
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dtype="float32",
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)
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temperature = paddle.ones([3], dtype="float32")
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speculate_calculate_logits_entropy(logits, share_inputs, temperature)
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print(share_inputs["entropy_list"])
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self.assertEqual(len(share_inputs["entropy_list"][0]), 0)
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self.assertEqual(len(share_inputs["entropy_list"][1]), 1)
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self.assertEqual(len(share_inputs["entropy_list"][2]), 0)
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self.assertEqual(len(share_inputs["entropy_list"][3]), 0)
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self.assertAlmostEqual(share_inputs["entropy_list"][1][0], 0.0024676250759512186, places=6)
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if __name__ == "__main__":
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unittest.main()
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